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Course Outline

Introduction to Edge-Based Artificial Intelligence for Computer Vision

  • Overview of edge computing capabilities and operational advantages
  • Comparative analysis: Cloud-based versus edge-based AI architectures
  • Primary challenges associated with real-time image processing

Deployment of Deep Learning Models on Edge Hardware

  • Introduction to TensorFlow Lite and OpenVINO for government use cases
  • Strategies for optimizing and quantizing models for edge deployment
  • Case study: Implementation of YOLOv8 on edge hardware

Hardware Acceleration for Real-Time Inference Operations

  • Overview of edge computing infrastructure (NVIDIA Jetson, Google Coral, FPGAs)
  • Utilization of GPU and TPU acceleration technologies
  • Methodologies for benchmarking and performance evaluation

Real-Time Object Detection and Tracking Systems

  • Implementation of object detection frameworks using YOLO models
  • Protocols for tracking dynamic objects in real-time environments
  • Enhancement of detection accuracy through sensor fusion techniques

Optimization Strategies for Edge Artificial Intelligence

  • Techniques for reducing model footprint via pruning and quantization
  • Methods for minimizing latency and energy consumption
  • Procedures for retraining and fine-tuning edge models

Integration of Edge AI with Internet of Things (IoT) Infrastructure

  • Deployment of AI models on smart cameras and IoT endpoints for government operations
  • Facilitating real-time decision-making at the edge
  • Data exchange protocols between edge devices and centralized cloud systems

Security Protocols and Ethical Frameworks in Edge AI

  • Addressing data privacy requirements in edge AI applications
  • Measures to ensure model resilience against adversarial threats
  • Adherence to regulatory standards and ethical AI principles

Summary and Future Directions

Requirements

  • Working knowledge of computer vision principles
  • Practical experience utilizing Python and deep learning frameworks
  • Foundational understanding of edge computing architectures and IoT devices

Audience

  • Computer vision engineers
  • Artificial intelligence developers
  • IoT specialists
 21 Hours

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